Titolo:  IntelliAV: Toward the feasibility of building intelligent anti-malware on Android devices
Autori: 
Data di pubblicazione:  2017
Autori:  Ahmadi, Mansour; Sotgiu, Angelo; Giacinto, Giorgio
Presenza coautori internazionali:  no
Lingua:  Inglese
Titolo del libro:  Machine Learning and Knowledge Extraction
ISBN:  9783319668079
Editore:  Springer
Serie:  LECTURE NOTES IN COMPUTER SCIENCE
Volume:  10410
Pagina iniziale:  137
Pagina finale:  154
Numero di pagine:  18
Digital Object Identifier (DOI):  http://dx.doi.org/10.1007/978-3-319-66808-6_10
Codice identificativo Scopus:  2-s2.0-85028984956
Codice identificativo ISI:  WOS:000455398500010
Revisione (peer review):  Esperti anonimi
Nome del convegno:  1st IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference on Machine Learning and Knowledge Extraction, CD-MAKE 2017
Periodo del convegno:  29 August - 1 September 2017
Luogo del convegno:  Reggio Calabria, Italia
Abstract:  Android is targeted the most by malware coders as the number of Android users is increasing. Although there are many Android anti-malware solutions available in the market, almost all of them are based on malware signatures, and more advanced solutions based on machine learning techniques are not deemed to be practical for the limited computational resources of mobile devices. In this paper we aim to show not only that the computational resources of consumer mobile devices allow deploying an efficient anti-malware solution based on machine learning techniques, but also that such a tool provides an effective defense against novel malware, for which signatures are not yet available. To this end, we first propose the extraction of a set of lightweight yet effective features from Android applications. Then, we embed these features in a vector space, and use a pre-trained machine learning model on the device for detecting malicious applications. We show that without resorting to any signatures, and relying only on a training phase involving a reasonable set of samples, the proposed system outperforms many commercial anti-malware products, as well as providing slightly better performances than the most effective commercial products.
Tipologia: 4.1 Contributo in Atti di convegno

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